Neural Networks: Step-by-Step Guide
Learn how neural networks work for credit approval decisions
Product Description
This product provides a glimpse into how neural networks work by demonstrating the fundamental process of learning from data. Neural networks take inputs and attempt to predict outputs by training models, adjusting their internal parameters (which include weights and biases) based on sample outputs. The network learns by comparing its predictions to known correct answers, gradually improving its accuracy. We use a real-life credit approval use case to illustrate this concept - showing how a neural network can learn to approve or decline loan applications by analyzing patterns in historical lending data, including factors like income, credit scores, and loan amounts.
A neural network is like a simplified version of how our brain works. It learns patterns from data to make predictions. In our case, we want to predict whether a loan should be approved or declined.
Imagine you work at a bank and need to decide whether to approve loans. You look at:
- • Look at income vs loan amount
- • Check credit score
- • Consider age and stability
- • Use experience and intuition
- • Make subjective judgment
- • Convert all data to numbers
- • Learn patterns from thousands of examples
- • Use mathematical weights and biases
- • Calculate probability of approval
- • Make consistent, data-driven decisions
Input
Loan Data
Processing
Neural Network
Output
Approve/Decline
The Data
How we represent loan information as numbers the computer can understand
The Network
How neurons connect and process information layer by layer
The Learning
How the network improves its predictions through training
1. What is a Neural Network?